code: def get_context_len(model_config: AutoConfig):
      """Determines the maximum length of the context (input + output tokens) based on the provided model
      configuration.
  
      Args:
          model_config (AutoConfig): The model configuration object containing information about the model's properties.
  
      Returns:
          int: The maximum context length, which can be derived from the model configuration. If no relevant attribute
               is found, the default value of 2048 is returned.
  
      This function examines the provided model configuration object to identify the attribute that specifies the maximum
      context length. It checks for attributes in the following order of preference:
      1. 'max_sequence_length': If this attribute is present in the model configuration, its value is returned.
      2. 'max_position_embeddings': If 'max_sequence_length' is not found but 'max_position_embeddings' is present, its
         value is returned.
      3. 'n_positions': If neither 'max_sequence_length' nor 'max_position_embeddings' are found, and 'n_positions' is
         present, its value is returned.
      4. Default: If none of the relevant attributes are present, the function returns a default value of 2048.
  
      Note:
      - The maximum context length is important for defining the size of input and output sequences in a model.
  
      Example Usage:
      >>> config = AutoConfig.from_pretrained("bert-base-uncased")
      >>> context_len = get_context_len(config)
      >>> print(context_len)
      512
      """
      if hasattr(model_config, "max_sequence_length"):
          return model_config.max_sequence_length
      elif hasattr(model_config, "max_position_embeddings"):
          return model_config.max_position_embeddings
      elif hasattr(model_config, "n_positions"):
          return model_config.n_positions
      else:
          return FALLBACK_CONTEXT_LEN
  
imports: import copy
  import logging
  import tempfile
  from typing import Dict, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn.functional
  import transformers
  from bitsandbytes.nn.modules import Embedding
  from packaging import version
  from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, TextStreamer
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.schema.trainer import LLMTrainerConfig
  from ludwig.utils.error_handling_utils import default_retry
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.model_utils import find_embedding_layer_with_path
label_desc: Use image node_id 4 for calling a global function with example usage: get_context_len(model_config) and returns: model_config, model_config, model_config, FALLBACK_CONTEXT_LEN